Definition
AI-Generated Virtual Environments refers to AI systems that generate complete, interactive virtual environments for consumption by humans, AI agents, and machines. This encompasses two distinct technical paradigms:
- World Models that understand dynamics and physics and generate interactive simulations
- 3D Asset/Environment Generators that produce exportable geometry, textures, and scene data compatible with game engines
The domain is characterized by:
- Rapid technological evolution (2024-2025 saw the emergence of “Large World Models” as a distinct category)
- Significant architectural diversity (video-based vs. geometry-based approaches)
- Fundamental questions about what “generation” means when outputs range from real-time video streams to batch-produced mesh files
World Model Disambiguation
The term “world model” itself has two related but distinct meanings:
| Type | Description | Examples |
|---|---|---|
| Generative World Models | Create external environments | World Labs Marble, Google Genie, NVIDIA Cosmos |
| Internal Predictive World Models | Reasoning systems for AI agents | Meta V-JEPA |
This report focuses on Generative World Models.
In Scope
The following are explicitly within the scope of this research:
- AI systems that generate complete environments, levels, or worlds (batch and runtime)
- Outputs intended for interactive consumption in engines and runtimes (Unreal, Unity, web viewers)
- Geometry-native outputs (meshes, textures) and neural-native outputs (Gaussian splats)
- Multi-user contexts where players and agents consume the same world state
- Hybrid workflows combining procedural graphs with neural generation
- Generation metadata, reproducibility, and validation profiles
- Multiplayer determinism requirements for AI-generated content
Out of Scope
| Topic | Reason | Covered By |
|---|---|---|
| Agent cognition and decision-making | Distinct domain with different standards needs | MSF Autonomous Agents Use Case |
| Provenance and attribution standards | Infrastructure dependency | MSF Transparency and Provenance |
| Cross-platform asset format standards | General interchange, not AI-specific | 3D Asset Interoperability WG |
| NeRF representations | No portable format, not real-time viable | Academic research |
| AR-specific concerns (spatial anchoring) | Distinct requirements | MSF AR Use Cases 18-21 |
| Robotics-only training scenarios | Not intended for metaverse consumption | Industrial simulation standards |
Adjacent Domains
| Domain | Relationship | Overlap Areas |
|---|---|---|
| Robotics/Physical AI | Shared technology, distinct purpose | World models (Cosmos), training environments |
| Digital Twins | Overlapping if multi-consumer | Industrial metaverse, enterprise simulation |
| Gaming/Game Engines | Core overlap | Primary deployment target |
| VFX/Film Production | Adjacent market | Pre-visualization, USD workflows |
| Procedural Content Generation | Evolution from | Hybrid AI+PCG workflows |
| 3D Asset Generation | Component/subsystem | Single-object generation within environments |
Key Terminology
AI system that understands dynamics and generates interactive simulations
- Large World Model (LWM)
- Foundation World Model
Explicit particle representation using millions of Gaussian distributions for real-time rendering
- Gaussian Splats
- 3DGS
- GS
Capacity for AI to understand and generate spatially consistent 3D representations
- 3D Intelligence
- Spatial AI
NVIDIA term for AI systems designed for physics-aware robotics and simulation
- Embodied AI
- Robotics AI
3D asset generation approach that reconstructs 3D from 2D images
- Reconstruction
- Structure from Image
World model approach that predicts future video frames from current state
- Video Prediction
- Frame Generation
3D content stored as neural network weights rather than traditional geometry
- Neural Representations
- Radiance Fields
Complete parameter set needed to reproduce AI-generated content (prompt, seed, model version)
- Generation Metadata
- Reproducibility Data